Clinicians and physicians, including specialists such as radiologists, gastroenterologists, and particular types of surgeons, may need access to all of a patient's medical information in order to make the best diagnoses and treatment decisions. More commonly, the specialist needs access to certain aspects of a patient's medical history, but may not know in advance, or even during examination, which aspects will inform decisions. In particular, background information about the patient and the patient's medical history can aid the specialist in interpreting the results of any performed studies. However, the manner in which information is organized and stored in an electronic health record system may not correspond to the set of concepts that a clinician must evaluate in order to put in place an optimal treatment plan for the patient at hand. As a practical example, prior to initiating a particular surgery—for example, vascular surgery of the carotid artery to reduce the risk of stroke—the clinician should understand if the patient is already on maximal medical (non-surgical) therapy for this particular condition. The concept of ‘maximal medical therapy for carotid artery vascular disease’ may be well described in the literature and is typically described as part of the clinical guideline. However, this example concept of ‘maximal medical therapy for carotid vascular disease’ is far too granular and specific to be encoded in the database schema at the heart of an EHR system—even the most highly structured EHR systems are unable to encode the myriad clinical concepts that drive optimal decision-making. Further, the substrate information that would inform the clinician about this concept might be scattered in multiple places in the patient's record. Continuing the example, this information for the concept of ‘maximal medical therapy for carotid artery vascular disease’ might include a description of the classes medication specified for this condition. Further information to be extracted from the medical record of the patient includes references to medications given, medications not tolerated by patient, medications for which the patient has an allergy. This information might be scattered throughout unstructured clinic (text) notes, structured medication lists, or semi-structured problem lists. And when present, especially in unstructured information that comprises the majority of EHR information, the clinical concept may be only embodied in complex medical language reference, understandable to a human reader, but not encoded as a structured piece of information. Further, the patient's medical information is typically dispersed across multiple data stores, which may be electronic or non-electronic. As a result, the information-gathering burden for a clinician is frequently overwhelming because of a mismatch between the data schema of the EHR and the practical, clinical concepts that the specialist must evaluate in order to initiate a treatment plan. As a result, clinicians frequently make complex clinical decisions with limited information on hand. This dearth of relevant information in decision-making leads to inappropriate utilization of expensive healthcare resources and can, in some instances, even compromise patient safety.
Searches of a patient's electronic health record (EHR) can be extremely time consuming. For example, in a radiological (medical imaging) study, EHR searching can consume 20-53% of the total interpretation time of the study (ref: Lin et al, Am J Roentgenol 2010). Similar EHR searches may be performed in 27-64% of all Abdominal-Pelvic CT (ABP-CT), Transvaginal Pelvic Ultrasound (TV-US), and Brain MRI (B-MRI) studies. In a relatively large hospital, every week many hundreds of these studies may be performed. Therefore, a large amount of time and health care money is spent manually searching patient medical records for ancillary medical data.
Furthermore, treatment guidelines exist that are based on previous medical studies, such as clinical trials or procedure results, and these guidelines could help the specialist ascertain the appropriateness and risk of a particular procedure in light of a patient's characteristics. Of course, a specialist does not retain all medical knowledge in his or her own head, and the scope of medical literature on any particular subject could be unwieldy to efficiently search. As a result of their length and complexity, guidelines, typically informed from extensive clinical trial information, are generally impractical for the clinician to utilize in real time for patient care. The reason is that each is comprised of up to hundreds of clinical scenarios described by numerous clinical concepts, as in the preceding example (e.g., the concept of ‘maximal medical therapy for carotid vascular disease’). In practice, the clinician is confronted by both an overwhelming complexity of decision guideline as well as an overwhelming information retrieval problem.
Therefore, it would be desirable to have a system and method for extracting clinical concepts required for a guideline from the medical record, making such information available to a given clinician, allowing him or her to evaluate these concepts and attest to their presence for a patient, and then reconciling the clinician's attestations against the description of best practice embodied in the clinical guideline.